CO₂ is a critical greenhouse gas, with fluctuations in its atmospheric concentration significantly influencing global climate. Effective monitoring of CO₂ emissions and accurately mapping the distribution of CO₂ sources and sinks are vital for managing atmospheric CO₂ levels and mitigating global warming. Satellite remote sensing technology offers the ability to detect global CO₂ distribution with high temporal and spatial resolutions. To improve the precision of CO₂ mixing ratio determinations, it is essential to simultaneously measure atmospheric O₂ concentration, utilizing the uniform mixing of O₂ molecules as a reference to calculate the CO₂ to dry air mixing ratio. Current orbital CO₂ remote sensing instruments primarily utilize the 0.76 μm O₂-A band for detection. However, the O₂(a¹Δ_g) band near 1.27 μm is a more suitable detection channel due to its proximity to the two CO₂ absorption bands at 1.6 μm and 2.0 μm, reducing uncertainties related to atmospheric path spectral variations; moreover, its weaker absorption spectra compared to the O₂-A band are less prone to saturation, yielding more accurate radiative transfer modeling and spectral fitting results. Despite the strong airglow radiation associated with the O₂(a¹Δ_g) band, which has historically rendered it impractical for global greenhouse gas measurements, this study explores its influence on CO₂ volume fraction inversion. We demonstrate that with high spectral resolution and adequate signal-to-noise ratio, the airglow spectral features of the O₂(a¹Δ_g) band can be effectively distinguished from the absorption spectral features, significantly improving the accuracy of satellite-borne CO₂ mixing ratio inversions.

The O₂(a¹Δ_g) band serves as the target source for conducting CO₂ satellite remote sensing detection, aimed at enhancing CO₂ inversion accuracy. Our approach involves analyzing the characteristics of high-resolution solar radiation spectra across different bands to ascertain the advantages of the O₂ absorption feature at 1.27 μm. These features reduce the uncertainty associated with wavelength-dependent atmospheric scattering and enhance radiative transfer model precision. We simulate solar scattering spectra and airglow radiation spectra using the atmospheric radiative transfer model, the HITRAN molecular database, and the photochemical reaction model, reflecting more accurately the conditions of satellite-based remote sensing observations. We integrate effective signal-to-noise ratios according to the spectral resolution of remote sensing instruments into the observational spectra. We then investigate the effects of airglow, signal-to-noise ratio, and spectral sampling interval on spectral fitting using an optimization algorithm under various signal-to-noise and spectral sampling scenarios.

The results show that with a high reference signal-to-noise ratio (R_SNref=1000), ignoring airglow radiation in spectral fitting leads to an error of about 9% and a relative standard deviation of about 10%. Including airglow consideration reduces the fitting error to about 0.1% and the relative standard deviation to about 0.2%, with the deviation primarily influenced by instrumental random errors (Fig. 6). In addition, accounting for airglow radiation results in a minimal relative standard deviation in spectral fitting results and low dependency on the spectral sampling interval when high inversion accuracy is maintained under high signal-to-noise ratio conditions. Conversely, under low signal-to-noise ratio conditions, the relative standard deviation significantly increases, showing fluctuations and a rapid rise with increasing spectral sampling interval (Fig. 7).

Despite the strong airglow emissions of the O₂(a¹Δ_g) band, a high-resolution (λ/Δλ=25000) satellite-borne spectrometer with a high signal-to-noise ratio can effectively differentiate its spectral features from those of O₂ absorption. The unique advantages of the 1.27 μm O₂(a¹Δ_g) band in carbon satellite applications indicate its significant scientific and engineering value in enhancing CO₂ satellite-borne detection. This band is poised to be a pivotal improvement for the next generation of carbon satellites, aiming for more precise and efficient monitoring of global atmospheric CO₂ concentrations.